Scraper

In [1]:
import os
from google_images_download import google_images_download 
In [2]:
root_dir ='data_food_classifier_MG'
In [3]:
response = google_images_download.googleimagesdownload()   #class instantiation

arguments_1 = {"keywords":"fast food, hamburger, kebab, pizza, french fries, chicken popeyes, burito, unhealthy food, unhealthy diet, fatty food, hot dog",
               "limit":100,
               "silent_mode":True,
               "format":"jpg", 
               "prefix":"fast_food",
               "output_directory":os.getcwd(),
               "image_directory":'data_food_classifier_MG'}   #creating list of arguments

response.download(arguments_1)   #passing the arguments to the function

arguments_2 = {"keywords":"healthy meal, vegetables, fruit, fit meal, walnuts dinner, fish dinner, green beans dinner, healthy diet, salad, diet food",
               "limit":100,
               "silent_mode":True,
               "format":"jpg", 
               "prefix":"slow_food",
               "output_directory":os.getcwd(),
               "image_directory":'data_food_classifier_MG'}   #creating list of arguments
response.download(arguments_2)   #passing the arguments to the function
Downloading images for: fast food ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 79 is all we got for this search filter!
Downloading images for:  hamburger ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 0 is all we got for this search filter!
Downloading images for:  kebab ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 86 is all we got for this search filter!
Downloading images for:  pizza ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 84 is all we got for this search filter!
Downloading images for:  french fries ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 88 is all we got for this search filter!
Downloading images for:  chicken popeyes ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 77 is all we got for this search filter!
Downloading images for:  burito ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 79 is all we got for this search filter!
Downloading images for:  unhealthy food ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 89 is all we got for this search filter!
Downloading images for:  unhealthy diet ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 92 is all we got for this search filter!
Downloading images for:  fatty food ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 0 is all we got for this search filter!
Downloading images for:  hot dog ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 86 is all we got for this search filter!
Downloading images for: healthy meal ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 88 is all we got for this search filter!
Downloading images for:  vegetables ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 0 is all we got for this search filter!
Downloading images for:  fruit ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 90 is all we got for this search filter!
Downloading images for:  fit meal ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 87 is all we got for this search filter!
Downloading images for:  walnuts dinner ...
Downloading images for:  fish dinner ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 91 is all we got for this search filter!
Downloading images for:  green beans dinner ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 0 is all we got for this search filter!
Downloading images for:  healthy diet ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 92 is all we got for this search filter!
Downloading images for:  salad ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 96 is all we got for this search filter!
Downloading images for:  diet food ...


Unfortunately all 100 could not be downloaded because some images were not downloadable. 0 is all we got for this search filter!
Out[3]:
({'healthy meal': ['C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 1.healthy-dinner-collection-main-image.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 2.IMG_16162-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 3.Healthy-Meal-Prep-.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 4.teriyaki-chicken-meal-prep-4.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 5.best-healthy-dinner-recipes-1567029863.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 6.healthy-chicken-fajitas-meal-prep-square.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 7.dfe06fb20721a3a25f7b515638c7db75.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 8.Blackened-Shrimp-Meal-Prep9.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 9.Meal-Prep-Chickpea-Salad1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 10.chicken-satay-salad.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 11.Crispy-Cauliflower-Tacos-036.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 12.Healthy-Meal-Prep-Baked-Turkey-Meatballs--500x500.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 13.HEALTHY-DINNER-IDEAS.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 14.maxresdefault.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 15.Korean-Chicken-Meal-Prep-Bowls-6.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 16.tarragon-pesto-e1526480069676-920x703.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 17.sun-basket.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 18.Whole_30_Chicken_Salad_010-sq.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 19.meal-prep-recipes.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 20.6-13_CookWithUs_ShopOnce_EatFiveTimes_Stocksy_EasyHealthyDinners_NadineGreff.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 21.healthy-meal-prep-recipes.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 22.healthy-meals-for-easy-meal-prep.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 23.lunch-round-up-new.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 24.28-Day-Healthy-Meal-Plan-800x1000.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 25.light-dinners-coconut-shrimp-rice-1566498330.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 26.delish-pan-fried-tilapia-377-1543266609.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 27.GettyImages-588978788.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 28.43-Healthy-Meal-Prep-Recipes.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 29.OLI-1018_Healthy-PiriPiriChickenMozzarellaStacks_28557-5cf6a85.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 30.healthy-lunch-ideas-1555339294.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 31.How-to-Meal-Prep-2.0-Pin-322x560.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 32.weight-loss-meal-plan.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 33.82a927f390635209a7fedb24c12b7c2e-Home_640x746_Healthy-Meal-Plans.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 34.Healthier-One-Pot-Sesame-Chicken-Healthy-One-Pot-Meals-Recipe-750x498.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 35.238055.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 36.RFO-472x310-ChickenTrayBake-acae5193-a2c9-4a58-9c9c-cee82a565474-0-472x310.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 37.Grilled-Chicken-Meal-Prep-Bowls-4-Ways-for-Clean-Eating.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 38.maxresdefault.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 39.20-Minute-Meal-Prep-Chicken-and-Broccoli-9.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 40.chickenparmstuffedpeppers1-1519936991.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 41.healthy-meal-plan-4.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 42.Green-Chef.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 43.Healthy-Meals-Mediterranean-Panzanella-Salad-thumb.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 44.5-easy-and-healthy-meal-prep-lunch-ideas-149262-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 45.fresh-summer-meal-plan.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 46.turkey-taco-lunch-bowls_Cheesy-Broccoli-Cheddar-Chicken-and-Rice-Bowls-Casserole-Meal-Prep.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 47.Portion-Plate.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 48.35-Healthy-Meal-Sized-Salads-You-Need-to-Make-Whole30-Keto-Paleo-Gluten-Free-Low-Carb-Recipes.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 49.hellofresh.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 50.1140-nutrients-food-loved-ones-caregiving.imgcache.rev17dd9e9578e4259ab90ca152af4057e9.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 51.105-Meal-planning-for-simple-quick-healthy-cooking_1081277720.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 52.Honey-Garlic-Chicken-Stir-Fry_16x9_Healthy-Meal-Plans-Thumb.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 53.Ground-Turkey-Meal-Prep-Bowls-4.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 54.Signature-Bowl.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 55.snack-boxes-5-copy.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 56.4-Healthy-Meal-Tips-for-Type-2-Diabetes-722x406.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 57.GRT-12865-22_Insanely_Easy_and_Quick_Healthy_Meals_for_One-1296x728-header-1296x728.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 58.mix-match-meal-prep-21.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 59.IMG_2708-copy.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 60.greek-healthy-meal-prep-whole30-paleo-3-700x1050.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 61.Stuffed-avocado.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 62.2017-31-05_Meal_Prep_Hero_Blog_730x485.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 63.Asian-Chicken-Rice-Bowl_EXPS_SDAS17_201063_D04_11_5b.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 64.healthy_meal_prep_time_saving_plans_to_prep_and_portion_your_weekly_meals-9781465464866.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 65.BFV13132_HealthyMeal-PrepChickenSaladPockets-ThumbTextless1080.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 66.6-ingredient-curry-chicken-meal-prep.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 67.best-healthy-dinner-recipes-1567029863.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 68.50-Quick-Healthy-Dinners-30-Minutes-Or-Less.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 69.healthy-meal-plan-index-1535040812.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 70.lunch-box-ideas-meal-prep.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 71.Meal-Prep-Roasted-Veggies-and-Chicken-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 72.1140-why-eating-dead-food-bad-for-health.imgcache.reve0bd88bfc1ba0dbcf7d3479ed798310f.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 73.1519936377-chickenparmstuffedpeppers1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 74.LighterNachoes_0_0_0.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 75.40healthyfoodblogs3-5c09845946e0fb00017bdd41.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 76.Turkey-Medallions-with-Tomato-Salad_EXPS_HCK17_109411_D08_26_7b.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 77.basil-pesto-chicken-pasta-meal-prep-bowls-pic.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 78.raw-pad-thai-choosing-chia.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 79.maxresdefault.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 80.Healthy-Burrito-Bowls-with-Cauliflower-Rice-023-800x1000.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 81.5-genius-tips-for-healthy-meal-planning-2-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 82.healthy-meal-prep-containers-quinoa-600w-791066038.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 83.insta-14-3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 84.30-min-dinner-ideas-HERO.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 85.a9daeb724e9ae12cd7044c8db9990d3c.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 86.slim-weightloss-image-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 87.Vegan-dinner-recipes-featured.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 88.preparing-healthy-salad-with-chia-seeds-on-rustic-wood-table-picture-id641582074.jpg'],
  ' vegetables': [],
  ' fruit': ['C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 1.assortment-of-colorful-ripe-tropical-fruits-top-royalty-free-image-995518546-1564092355.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 2.Fruit-Salad-SWP.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 3.What-Your-Favorite-Fruit-Says-About-You.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 4.Culinary_fruits_front_view.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 5.eat-your-fruits-and-veggies-to-avoid-strokes.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 6.Low-Carb-Fruits.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 7.fruits_feature.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 8.maxresdefault.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 9.fruit_selection_155265101_web.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 10.fruits-compressor.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 11.fruits-tropicals-marguery-exclusive-villas-1200x700.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 12.all-fruit-contains-sugar-but-generally-less-that-sweetened-food.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 13.fruit.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 14.Apricots.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 15.Fruits.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 16.shutterstock_232878598.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 17.51eB6A%2BNrwL._SX325_BO1,204,203,200_.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 18.47429859_303.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 19.Indian_jujube_%28fruit%29.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 20.Fruit-kebabs.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 21.maxresdefault.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 22.Fruit-Pizza-Design-Square.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 23.fruits-kFLF--621x414@LiveMint.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 24.Fresh-fruit-pretty.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 25.strawberries.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 26.Fruit+vegetables_800_480_85_s_c1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 27.creative-layout-made-fruits-flat-260nw-1017075634.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 28.Bananas-5c6a36a346e0fb0001f0e4a3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 29.neede_contamination_spreads_to_other_fruits.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 30.alt-5a09a199cbf24-4499-a98896d5d4f7c07fce87ef1e138011ee@1x.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 31.Fruit-Platter-a.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 32.Fresh-Fruit-Bowl-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 33.is-avocado-a-fruit-or-a-vegetable-1296x728-feature.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 34.Fruit-Salad-Honey-Lime-Dressing-IMAGES-223.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 35.colorful-fruits-vegetables-clipart-set-fruit-vegetable-colored-cartoon-collection-vector-123176248.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 36.raspberries-pattern-royalty-free-image-1026593494-1564091639.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 37.IMG_5499-fruit-pizza.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 38.maxresdefault.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 39.healthy-fruits-1296x728.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 40.image-1-image-1-copy-mask.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 41.an-129208499.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 42.47425871_401.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 43.raisins-vert-380.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 44.thinkstock_photo_of_grenadilla_passion_fruit.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 45.iStock-636877252.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 46.rambutan-2477586_960_720.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 47.Best-Fruit-Salad-3-500x500.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 48.Kiwi_A-Z%20Fruit13.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 49.How-to-store-fruit-to-keep-it-fresh-resized.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 50.Honey-Glazed-Fruit-Salad-Family-Fresh-Meals-Recipe.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 51.How-to-make-the-BEST-Fruit-and-Cheese-Board-Full.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 52.pumpkins-1529604270.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 53.d8e6cf85-6ddb-40ca-b382-e7ba6f49217a-fruit.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 54.fruit-salad-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 55.best-fruit-salad-honey-lime-dressing-500x375.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 56.04-pomegranates-Fruits-and-Vegetables-that-Taste-Best-in-the-Fall_560360356-Tosa.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 57.pumpkin-a-fruit-hero.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 58.20190719-140436-blackberry_79345.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 59.Honey-Lime-Fruit-Salad1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 60.mango-620x350_620x350_71505731672.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 61.mixed-fruits.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 62.28_Grapefruit_Can-You-Identify-Everyday-Objects-By-These-Close-Up-Pictures-_398660866_Africa-Studio.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 63.es_strawberries_809.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 64.Worst-Fruits-Weight-Loss.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 65.920x920.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 66.image.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 67.Pomme.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 68.grapes_3275188k.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 69.several-kinds-of-whole-and-cut-citrus-on-a-pink-royalty-free-image-641256498-1558619183.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 70.6137879756_4466681697_o.0.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 71.Layered-Fresh-Fruit-Salad_EXPS_HCA18_2778_B04_26_3b-696x696.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 72.homemade-fruit-popsicles-1200-480x270.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 73.today-low-carb-fruit-veggies-tease-001-161117_5805c489bf7cdb5c39e2b98cc82e1e5f.fit-760w.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 74.Trinity-Fruit-Company-Mandarins-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 75.GettyImages-653271350_1024.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 76.Bananas.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 77.01_oranges_Finally%E2%80%94Here%E2%80%99s-Which-%E2%80%9COrange%E2%80%9D-Came-First-the-Color-or-the-Fruit_691064353_Lucky-Business-1024x683.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 78.29942-gettyimages-155302141.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 79.Friedas_Passion-Fruit-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 80.citrus.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 81.iStock-916071874-PERSIMMON.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 82.4923.fruit.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 83.Banana-Before-Bed-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 84.frozen-fruit-smoothie-3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 85.istock-950322084.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 86.WEEKLY_FRUIT_CONTAINER_1-copy_1b74faffbe944b0675f0e20473d3ad34.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 87.598257-harvard3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 88.54eaf80b81319_-_10-exotic-fruits-you-ve-probably-never-tried-mdn.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 89.fresh_fruit_salad_61942_16x9.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 90.hor-moonshot-apeel-sciences-01-1059213238.jpg'],
  ' fit meal': ['C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 1.62219982_2383149915274071_7911417252010786816_o.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 2.cenital-prep-e1468608273986.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 3.648666672389861221269607538682705299111936n---w-220.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 4.72603529_772510989847265_8675064633351570402_n.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 5.fit-meal-catering-1030x687.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 6.IMG_20161024_114754_.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 7.maxresdefault.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 8.4b1aa4_catering-dietetyczny-lodz-fit-meal-zdjecia.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 9.0040d7_catering-dietetyczny-lodz-fit-meal-uslugi-i-firmy-zdjecia.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 10.sc600x600.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 11.WildeKitchen_delivery_web-54_1800x.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 12.0f2a94_catering-dietetyczny-lodz-fit-meal-pozostale-uslugi-zdjecia.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 13.home_banner1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 14.Header_2_8da47466-a115-4931-a48c-022ebe14be82_2000x.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 15.c700x420.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 16.b3c51f90c09d0e2a79e8fbd5053c598e_featured_v2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 17.54521323_2329523973969999_1751218386039209984_o.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 18.easy-meal-prep-option-perfect-fit-meals-5.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 19.wp-1468173257176-862x645.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 20.50-WAYS-TO-MEAL-PREP.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 21.dieta-fit-meal-1030x665.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 22.IMG_20171130_004844_879-862x862.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 23.6521678023921790510378248847306998224519168n---w-220.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 24.c700x420.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 25.medifoods-damansara-kim8.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 26.sc600x600.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 27.keto_lite_6.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 28.1_week_1_meal.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 29.keto_5_nv_mains_salad_.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 30.A1KiZqNW5XL.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 31.paleo.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 32.fit-meal.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 33.Resized_IMG_36921.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 34.background.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 35.Salsa-Chicken-Meal-Prep-V.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 36.Meal-Plan-Template-1000x1500.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 37.Sides_1024x1024.JPG',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 38.284FF_JULY_205140.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 39.fit-meal-gallery-7.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 40.7-lean-meals-labrada-image-1-header-960x540.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 41.Beef-Bacon-and-Sweet-Potato-Casserole-and-Greek-Salad-with-others-3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 42.keto_5_v.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 43.screen-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 44.maxresdefault.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 45.dieta34.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 46.q-1030x688.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 47.facebook-cover-art.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 48.meal-prep-recipes.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 49.Healthy-Wraps-Fit-Camp-Foods-Signature-Vegan-Wrap_2000x2000.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 50.CjYUVeiWYAA0vcA.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 51.soulstorm%2Beverywhere%2Bplacki%2Bz%2Bcukinii.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 52.havana-chicken-breast.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 53.Chicken-Fajita-Bowl-490.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 54.athlete-kitchen-cardiff-meal-delivery-cardiff-0006_319bf9fa-a480-4431-8216-65a8830700a4_1024x1024.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 55.maxfitmeals_chickenfrittata2017_web.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 56.c700x420.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 57.e15767_fit-meal-catering-dietetyczny-lodz-uslugi-i-firmy-zdjecia.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 58.screen-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 59.RB-Fit-Kitchen-Bowls-Beef-with-Broccoli-12326434-43924707-3DHZL_Package_2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 60.1600-calorie-meal-plan-March-21-e1523288627343.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 61.0115-fitness-feature-snap-kitchen-enchiladas_oiiwc8_yfmjdd.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 62.9781760524579.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 63.heart-shape-by-various-vegetables-and-fruits.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 64.65213364_568024313735038_905220114897436672_n.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 65.FRESHFIT-57838_8.5x11.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 66.Salsa-Chicken-Meal-Prep-Containers-Three.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 67.74b153_fit-meal-catering-dietetyczny-lodz-lodzkie-zdjecia.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 68.CjYUVqKXAAA76KI.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 69.ls.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 70.53109529_122646818873350_3349053776531526006_n.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 71.c700x420.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 72.13_mag_web.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 73.70476628_387200028871139_943500701049246496_n.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 74.61923751_2375684829353913_7341658014488723456_o.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 75.201808232021321.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 76.EFG-Image-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 77.mandarin-chicken.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 78.fa122bf8318a88b3a1d1fbfba019b622.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 79.Fit-with-Diabetes-Meal-Plan-4-768x400.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 80.keto_5_day_v_1_copy.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 81.c700x420.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 82.maxfitmeals_breakfastburrito2017_web.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 83.Fresh-Fit-Meal-Plan-Guide-Week-8.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 84.f011251c80a40e9831529e64ec79d251.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 85.Hawaiian-Bowls-Fed-and-Fit-5-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 86.mealprep-packed-1-1030x787.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 87.23.jpg'],
  ' walnuts dinner': ['C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 1.Mac_and_cheese1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 2.Dark-Chocolate-Walnut-Berry-Snack-Mix-3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 3.walnut-crusted-chicken-4.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 4.Creme-de-la-Crumb-apple-cranberry-walnut-salad.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 5.walnut-crusted-chicken.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 6.Baked-Chicken-with-Rice-Pilaf-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 7.Walnuts_oyster_Caesar.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 8.walnuts_annotated-63c0eef716ae4b58a0826798f42b2cc2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 9.walnut-crusted-chicken-5.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 10.Pork-Medallions-with-Prunes-and-Walnuts-6.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 11.170501-easy-salad-with-pears-dried-cherries-and-candied-walnuts-vegan-glutenfree-salad-recipe-healthy-summer-ac-555p_f7f58dbcf568415ec5ce797544abd09b.fit-760w.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 12.Kale-with-pan-fried-walnuts.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 13.c2f65dc350d4acb31dd34e74e055eb77.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 14.9e5ef77d-d987-496b-9413-14a762e478c1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 15.Baked-Pork-Chops-with-Apples-pin-683x1024.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 16.Kumara-bacon-orange-and-walnut-salad-3-1200x1200.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 17.blogger-image-288148026.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 18.Panda-Express-Honey-Walnut-Shrimp-M-501x1024.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 19.024title-586x900.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 20.depositphotos_207070568-stock-photo-tasty-green-beans-walnuts-tomatoes.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 21.28428e644a76fe4aa990300b0e5d4a06.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 22.Kale-Pasta-with-Walnuts-and-Parmesan.-A-quick-and-healthy-one-pan-dinner-600x869.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 23.chicken-poblano.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 24.California-Walnuts-Virtual-Dinner-Party-3-590x393.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 25.Candied-Walnuts.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 26.healthy-walnut-recipes.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 27.duck-I-charlie-richards.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 28.Walnut-Crusted-Salmon-Sheet-Pan-Dinner.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 29.PearWalnutRavioli1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 30.Honey-Walnut-Shrimp-2-680x454.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 31.Ravioli-with-sauteed-asparagus-serving.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 32.crostini-with-goat-cheese-and-toasted-walnuts.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 33.sheet-pan-pesto-chicken-veggies-walnuts-4-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 34.honey-walnut-shrimp.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 35.VEGAN-XMAS-DINNER-BUTTERNUT-SQUASH-WALNUTS-AND-CRANBERRY-PIE-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 36.tasty-green-beans-walnuts-tomatoes-served-dinner-table-122735689.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 37.meal-prep-greek-chicken-cranberry-salad-img1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 38.Califlower-Pasta-100-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 39.26COOKING-SALAD2-articleLarge.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 40.tasty-green-beans-walnuts-tomatoes-served-tasty-green-beans-walnuts-tomatoes-served-dinner-table-122735774.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 41.dinner-menu-crispy-mushrooms.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 42.coconut-rice-salad-recipe.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 43.ketoSnackWalnuts-1137357088-770x553-650x428.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 44.Salted-Caramels-with-Walnuts-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 45.R35Q2PQ5UMY3JCLRCDAUJPQKWI.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 46.california-walnuts-weeknight-dinner-small-10.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 47.500_F_211345487_0wD1MhpOngYpIfat6EhRccgkBen1hz6Y.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 48.German-Walnut-Shortbread-2-688x1032.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 49.89139d6b2623b12d8a453e85856a38d5.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 50.prawns-with-honey-glazed.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 51.recipe-19903_Large400_ID-1519576.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 52.WH-Fall-in-Love-180-920x920.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 53.500_F_211345223_WqCsSKLvoXfVuvwYxfAroqYFhpEpXplt.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 54.AMAZING-Vegan-Taco-Meat-made-with-nuts-10-minutes-9-ingredients-BIG-flavor-vegan-glutenfree-mexicanfood-recipe-taco-7.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 55.500_F_211345503_SkqasGYL2KhqTnLPSe0HuGqNwLzNmUm4.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 56.Butternut-Squash-Lasagna-slice-with-pan.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 57.Image.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 58.Microwave-Spaghetti-Squash-Side.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 59.660.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 60.cheese-plate-delicious-mix-walnuts-honey-wooden-table-tasting-dish-food-wine-buffet-gala-dinner-149901038.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 61.Chicken-Stuffed-with-Walnuts-Apples-Brie_exps49248_CW1794338B04_15_1bC_RMS.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 62.94971747-bowl-of-beet-salad-with-feta-arugula-and-walnuts-on-white-dinner-table-concept-of-healthy-diet-eatin.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 63.grainsaladwithtoastedwalnutsdatesandgrapefruit15643891381564389150.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 64.red-walnuts-web.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 65.dinner-under-the-walnuts.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 66.cheese-plate-delicious-cheese-mix-with-walnuts-honey-on-wooden-table-tasting-dish-on-a-wooden-plate-food-for-wine-buffet-at-the-gala-dinner-TCEX09.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 67.salamagundy-chicken-oysters.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 68.500_F_211345490_NitHFmtY45YhOdA8q4djoMVNYo1GNaWa.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 69.Roasted-Beet-and-Kale-Salad-1000-4.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 70.Cherry-Walnut-Chicken-Salad-23-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 71.coconut-curry-lentil-walnut-meatball1500x1000.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 72.fc53sp070-02-main.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 73.Skillet-Pasta-with-Sun-Dried-Tomatoes-Walnuts-and-Feta-V1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 74.Raw-Bowl-with-Walnuts-Coconut-Chips-Lead.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 75.ricotta-gnocchi-photo.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 76.walnuts-2993134_960_720.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 77.butternut-squash-walnuts-vanilla-horiz-a-1800.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 78.cheese-plate-delicious-cheese-mix-with-walnuts-honey-on-wooden-table-tasting-dish-on-a-wooden-plate-food-for-wine-buffet-at-the-gala-dinner-TCEX1H.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 79.47acff0a3cf482972a7a25745dd28ace.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 80.slices-ham-walnuts-cheese-plate-knife-fork-near-dish-white-stones-background-luxury-dinner-97529850.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 81.Pomegranate-Pear-Salad-with-Walnuts-Plated-Cravings-7.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 82.ChickenWalnutSalad-Slide5.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 83.Candied-Walnuts.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 84.breakfast-lunch-banner.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 85.walnut-salad-1-500x500.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 86.126087314-a-gourmet-dinner-a-plate-of-grilled-camembert-with-spinach-walnuts-and-smoky-tomatoes-various-appeti.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 87.Peach-panna-cotta-with-crispy-crumbles_800x800.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 88.imag0975-final.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 89.walnutleafnut.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 90.cauliflower-title-wp.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 91.loaded-blue-cheese-burgers-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 92.cheese-plate-delicious-cheese-mix-with-walnuts-honey-on-wooden-table-tasting-dish-on-a-wooden-plate-food-for-wine-buffet-at-the-gala-dinner-TCEX11.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 93.recipe-image-legacy-id--488535_11.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 94.Roasted-Beet-and-Kale-Salad-1000-5.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 95.500_F_204395669_rL1qSCntt9oW4zrV8Zc5QYbid9Y6kw0S.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 96.shrimp+(2).jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 97.Roasted-Root-Vegetable-Hash-with-Eggs-Bacon-Feta-and-Walnuts-makes-a-perfect-breakfast-OR-dinner.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 98.this-bright-beautiful-salad-with-salmon-walnuts-cranberries-and-oranges-is-the-perfect-easy-healthy-lunch-or-dinner.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 99.tumblr_inline_pljz1otI0g1qcybup_500.jpg'],
  ' fish dinner': ['C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 1.1440009947-weeknight-dinner-snapper-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 2.Date-Night-Fish-For-Two-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 3.1504633899842-recipeMainImagePath.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 4.Whole-Grilled-Fish-8.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 5.96883f77-82e7-4b57-9b88-3178d542f58b.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 6.grilled-branzino-4069001_bountygrilling021.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 7.easy-lemon-butter-fish-0.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 8.Friday-Fish-Dinner-4.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 9.salmon.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 10.valentines-day-dinner-baked-salmon-recipe.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 11.two-fish-roast.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 12.Best-Ever-Fish-Recipe_SQUARE2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 13.fish-and-chips-sheet-pan-dinner.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 14.Air-Fryer-Fish-22.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 15.fish%20on%20plate%2016x9.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 16.baked-salmon-4.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 17.89336_640x428.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 18.whole-baked-fish-with-lemon-herbs-and-garlic-butter-recipe-image-14.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 19.easy-peasy-fish-dinner-f-500x500.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 20.Fish-Packets-with-Snap-peas-Tomatoes-and-Herb-Butter-Back-to-School-Grocery-Bag-14082017.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 21.Parchment-Pouch-Fish-Dinners-with-Fingerling-Potatoes-5.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 22.Salmon-with-Creamy-Dill-Sauce_EXPS_GHBZ18_22391_C08_09_8b.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 23.IMG_0426-Portugal-Fish-dinner.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 24.shutterstock_639457966_fish.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 25.208079_3000x2000.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 26.478x640_ac.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 27.exps36351_SD1440068D17.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 28.food-fish-fresh-dorado-meal-seafood-dinner-raw-delicious-food-fish-fresh-dorado-meal-seafood-dinner-vegetables-raw-delicious-139229163.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 29.Healthy-Cod-Fish-6.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 30.30-Mins-Mexican-Tilapia-Fish-Dinner-7.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 31.salmon-518497_1280-1080x675.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 32.Greek_Style_Baked_Fish_Final_3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 33.best-fish-recipes-shrimp-asparagus-stir-fry.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 34.maxresdefault.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 35.Broiled-Cod-With-Fennel-and-Orange-05122016.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 36.jo_easy5_hero_one-pan_fabulous_fish_168_s600x600_c3456x2019_l0x1592.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 37.fish-slimming-world-chips-with-mushy-peas_sw_recipe.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 38.ecd036e18fdae3ad8112cd70bd443f80.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 39.fish-dinner.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 40.Baked-Fish-and-Chips.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 41.Nordic-Trip-Iceland-An-Easy-One-Pan-Tilapia-Dinner...And-A-Dash-of-Cinnamon.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 42.coconut-fish-curry.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 43.lunch-dinner_entrees_classic-lemon-pepper-fish.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 44.Fish-Dinner-Grilling-Eating-Natural-Food-Grill-2073798.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 45.Sheet-Pan-Cod-blog-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 46.mediterranean-sheet-pan-fish-dinner-735x1103.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 47.Easy_Parmesan_Crusted_Fish_Dinner.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 48.picwuqOh7.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 49.healthy-fish-dinner-184844201-59fdd12d13f12900370f887b.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 50.tilapia-packets.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 51.R6GGMMZCWIY2TGT7ATVCODY2WQ.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 52.6da43999-a981-420d-ab1b-459d1fa60266.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 53.breaded_fish_penzeys.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 54.fish-tacos-5.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 55.maxresdefault.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 56.Lemon-Butter-Baked-White-Fish-Dinner-Recipe-500x500.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 57.Fish-Dinner-for-Eight-OH-Vert.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 58.sweet-chili-fish-wrap-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 59.fish-baked-f.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 60.Mediterranean-Fish-Burger-Dinner-Board-2-700x1049.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 61.Pan-Seared-Salmon-with-Dill-Sauce_EXPS_SDAS18_133878_C03_29__10b.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 62.garlic-butter-fish-pin.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 63.eddcabc6-ca86-4c6e-b690-076c51bd68a3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 64.fish-packet-asparagus-and-rice.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 65.500_F_21496222_xsBGaDdbKfPaWiYVOQRvwu3ug4WKkiZY.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 66.Broiled-Miso-Cod-foodiecrush.com-052.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 67.e855811382dbf89af085302b44e4c537.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 68.23-delicious-fish-recipes-for-busy-weeknights-1-8075-1394547815-9_big.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 69.seafood-meal-on-ice-EJ.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 70.Fish-Foil-Dinner-IG.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 71.DSC00837.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 72.Butter-Olive-Oil-Panko-Crusted-Fish-Dinner-Lady-Behind-The-Curtain-CollectiveBias-shop-6-320x320.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 73.SUQSO5FK24YEJGQGXWMINZXU64.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 74.fish-1101436_1280-1024x682.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 75.lemony-baked-haddock-recipe-photos-tablefortwoblog-6.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 76.1514993728-seared-salmon-lentil-salad-ghk-0118.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 77.Fennel-Crusted-Halibut-with-Asparagus-200-6.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 78.15056fish_dinner.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 79.640x478_ac.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 80.honey-garlic-salmon-3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 81.fishsticks1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 82.Parchment-Pouch-Fish-Dinners-with-Fingerling-Potatoes-3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 83.20110904-127355-dinner-tonight-whitefish-breadcrumbs.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 84.fish-stew_0.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 85.Fried-Fish-Dinner.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 86.FriedFishDinner_LRG_1024x1024.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 87.Kid-Friendly-Fish-Tacos-from-Walking-on-Sunshine-Recipes.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 88.2700.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 89.saltfish%20leed.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 90.simple-fish-and-soba-noodle-dinner.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 91.italian-fish-and-chips-picture-1-of-1.jpg'],
  ' green beans dinner': [],
  ' healthy diet': ['C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 1.good-vs-bad-food-unhealthy-junk-iStock_000058524328_Medium.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 2.healthy-eating-pyramid-350.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 3.balanced-diet-for-women-main-image-700-350.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 4.hd-what-is-the-best-heart-healthy-diet-plan-hd-cover.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 5.p6_BadFoodGoodFood_HL1801_dt52432050.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 6.eating-healthy.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 7.v3km5qmf-1400808066.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 8.Eat_Healthy_Blog_15May19-1200x800.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 9.INPQRTSEAZGKBH62LVG76M6TSE.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 10.health_cost_0804.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 11.iStock-1067777244.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 12.iStock-854725372-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 13.alimentation-equilibree.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 14.large_iStock_000015224885XSmall_veg_heart.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 15.healthy-buddha-bowl-lunch-with-grilled-chicken-royalty-free-image-920931456-1541086908.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 16.iStock-854725402-e1551812206105.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 17.50-super-healthy-foods-1296x728-feature.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 18.popular-healthy-foods-laid-out-on-white-background.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 19.healthy-heart-food-guide-hero_737_553_c1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 20.mnatgbetcxch5cb41fa2d903a.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 21.nutritional-rainbow-diet-cancer.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 22.ways-to-stick-to-a-diet-1296x728-feature.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 23.hero_0.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 24.The%20TABLE%20cover%20image.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 25.ht-heart-health.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 26.Clancy-Group-Healthy-Diet-Choices.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 27.ba087c1c-1459-4f3d-a64a-dd3c6298af27-large16x9_healthy.JPG',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 28.gettyimages-76127881.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 29.10-Keys-to-a-Healthy-Diet-3-750x500.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 30.balanced-diet-organic-healthy-food-clean-eating-selection-including-certain-protein-prevents-cancer-931193062-799da546cdb9457e91a0e88fa8a31eac.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 31.iStock-1131794876.t5d482e40.m800.xtDADj9SvTVFjzuNeGuNUUGY4tm5d6UGU5tkKM0s3iPk-620x342.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 32.healthy-diet-good-wellbeing.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 33.food_pyramid.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 34.KidsHealthyEatingPlate_Jan2016.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 35.This-Is-What-a-Healthy-Diet-Will-Look-Like-in-2018-9.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 36.2469_16-Positive-Effects-Of-Healthy-Eating-On-Your-Life_ss.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 37.150506112204-fruits-nuts-vegetables-grains-stock-large-169.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 38.EatwellPlate_0.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 39._13106ace-67e8-11e8-8033-47bccc77d658.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 40.heart-healthy-diet.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 41.83345c49af9718c9dcb1d4f6e1f32642.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 42.iStock-854725400-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 43.Healthy_Eating_A_Detailed_Guide_for_Beginners-732x549-thumbnail.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 44.photo-1490645935967-10de6ba17061.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 45.bigstock-Fruits-And-Vegetables-36840977.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 46.i1080x475.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 47.vegetables-fruits-whole-grains-Healthy-eating-large-bigstock.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 48.Marketvegetables.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 49.Top-5-Nutrition-Foods-for-Fitness-and-Healthy-Life.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 50.T3VHEERIDNBQNITRT5PTLRLOUA.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 51.ThinkstockPhotos-179103931-Diet-2048x1024.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 52.veggies.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 53.food-pyramid-healthy-diet-nutrient-pyramid.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 54.fruit-veg-shopping.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 55.summer-2018-main-plan-menu-chart.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 56.iStock-919666108-2_field_img_intro_774_500.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 57.diet-tips9.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 58.mcdc6_mayo_clinic_healthy_weight_pyramid-8col.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 59.Healthy-Heart-Diet-Chart.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 60.methode%2Ftimes%2Fprod%2Fweb%2Fbin%2F50dacfa0-944f-11e8-85e3-d844d3177259.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 61.maxresdefault.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 62.Featured-Image6-1024x576-1024x585.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 63.5-reasons-to-add-color-infographic-plus-color-english.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 64.fhm03.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 65.10%20healthy%20foods%20to%20include%20in%20your%20diet%202.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 66.Plant-Based-Diets-blog-cover-300x300-2018-02.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 67.0fde15f4c8bca289e4899768d340c015.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 68.healthy-salad.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 69.veggies_healthy_food_uns-1280x720.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 70.1._dengue_food_star_graphics.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 71.Berkeley-Life-Wellness-Hub-Heart-Vegetables-and-Fruit-Standard-Licence-123RF-61927140-Exp_-No.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 72.UACF_EG_Hero_NoBadge_Healthy-Eating-752x472.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 73.5e6a2697-3_slide-372820c5134d2d2410db537b72f81b0b43dcd388-s800-c85.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 74.Diet%20nutrition%20vegetables.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 75.healthy-meals-for-children-8ebb85b66ecb9e20a24c808d39a3745d6cdc7d4b0cfb6dc3b2c3307d783affac.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 76.shutterstock_672702967.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 77.a-person-sitting-on-the-floor-eating-a-healthy-salad.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 78.healthy-avo-food.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 79.p1chnnl9ih1kvh186p17sf1fj9dsg3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 80.Stocksy_txpa269f3e29MP100_Medium_1257410-58c70b893df78c353cdeb0bd.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 81.balanced-diet.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 82.shutterstock_278689922.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 83.imgbin-health-food-healthy-diet-health-Z968tmL9CkKQpgfWxDBVPfe0m.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 84.Healthy-eating-300x300.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 85.scott-warman-525481-unsplash-768x512.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 86.choosing-healthy-foods-inline.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 87.kbe02apg_low-calorie-vegetables_625x300_26_April_19.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 88.Salad---Pixabay.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 89.veggie-heart.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 90.womencommunicationdinnertogetherconceptppc49al.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 91.4288.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 92.diet-and-environmental-impacts-39mir6sjgyh0w3gm23ri80.jpg'],
  ' salad': ['C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 1.Big-Italian-Salad-760x983.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 2.Autumn-Harvest-Salad-Recipe-with-Sweet-Potatoes-Avocado-Cranberries-and-Pecans-1-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 3.exps6498_MRR133247D07_30_5b_WEB-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 4.Greek-Salad.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 5.classic-italian-salad-10_ba740feb132926901f5efa7bc2de7ad0.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 6.Everyday-Green-Salad-Recipe-3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 7.halloumi-salad-0619din.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 8.quick-chopped-salad-recipe-photos-tablefortwoblog-3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 9.Caesar-Salad-Fifteen-Spatulas-3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 10.salad_verte_with_15684_16x9.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 11.sandra-lee-food-today-main-181018-02_28c1f1d7033c651ae8bd93a89f929201.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 12.4552561.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 13.caesar-salad-10-1200.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 14.931-diabetic-powerhouse-kale-salad_designed-for-one_071118_3547183137.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 15.mexican-chopped-salad3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 16.Romaine-Avocado-Chicken-Salad-Recipe-3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 17.mediterranean-chickpea-salad-fb-ig-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 18.caprese-salad-recipe.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 19.wickedspatula-easy-tossed-big-italian-salad-recipe-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 20.Caesar-Salad-Recipe-3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 21.Grilled-Chicken-Cobb-SaladIMG_9150.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 22.Everyday-Mexican-Salad-Recipe-2-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 23.Couscous-Summer-Salad-Feature-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 24.t-mcdonalds-Premium-Southwest-Salad-with-Buttermilk-Crispy-Chicken.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 25.beetroot_halloumi_salad.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 26.Easy-Apple-Salad-Recipe-2-1200.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 27.Fattoush-Salad-Recipe-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 28.best-side-salad-3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 29.daphne-oz-today-main-190730-03_dcaea29aa7f98f11b9e2cf0696d9f9a2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 30.Mediterranean-Chopped-Salad-Culinary-Hill-5.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 31.Feta-Romaine-Salad_exps37614_SD2847494A02_12_9bC_RMS.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 32.Summer-Broccoli-Salad-1-725x725.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 33.dads-greek-salad-horiz-a-1600.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 34.201011-xl-jordons-romaine-salad.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 35.cucumber-tomato-salad-4.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 36.RFO-1400x919-Coriander--lime-cucumber-salad-with-chicken-aa3855fc-380a-4b44-9f88-b57a36bf2a88-0-1400x919.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 37.1.-Korean-Green-Salad.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 38.beetroot-feta-grain-salad.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 39.salad-done-right-FT-RECIPE0319.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 40.recipe-229.700x525.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 41.best-watermelon-salad-recipe-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 42.GoLive-Amiel-Cottage-Cheese-Salad-Lede.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 43.Skinny-Pink-Salmon-Green-Salad-Recipe-1-700x934.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 44.cuban-pasta-salad-12.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 45.healthy-kale-brocoli-salad-lemon-dressing-recipe-.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 46.recipe-185.700x525.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 47.Al-Desko-Steak-Salad-Leftovers-Recipe.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 48.12superfoodssalad-9-720x405.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 49.stone-house-salad-recipe-2255-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 50.1862-1210-CL-Chicken-Salad-V2-78524.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 51.simplest-green-salad-LEDE.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 52.Thai-beef-salad-5.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 53.Everyday-Greens-Salad.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 54.types-of-salad-cobb.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 55.olive-garden-salad-with-copycat-dressing-3-of-8.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 56.fruit-and-vegetable-salad-served-in-lettuce-leaf-500x375.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 57.Guacamole-Tossed-Salad_EXPS_MRMZ16_21265_C09_09_3b-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 58.t-mcdonalds-Premium-Bacon-Ranch-Salad-with-Grilled-Chicken.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 59.Edamame-Salad-8501811-February-24-2019-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 60.avocado_salad_60227_16x9.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 61.Easy-Green-Salad-4-500x500.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 62.Low-FODMAP-Cobb-Salad-4.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 63.Fruit-and-Berry-Salad1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 64.Crispy-Buffalo-Ranch-Chicken-Salad-with-Goddess-Dressing-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 65.Waldorf-Salad-4.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 66.burrata-salad-recipe-5.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 67.Chopped-Salad-008-800x1000.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 68.3_Ingredient_Hummus_Dressing_FromMyBowl-5.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 69.Italian-Summer-Salad_5_600X900.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 70.salad_005-1-555x740.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 71.Classic-Chicken-Salad-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 72.Cauliflower_Salad_08-web.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 73.Watermelon-Salad-Poppyseed-Dressing.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 74.horiatiki-greek-salad-today-041618-tease_79c5041ae6a58da5e333029bbe2c4b88.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 75.P4111233-copy.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 76.Antipasto-Salad-recipe-600x776.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 77.mexican-wedge-salad_thecozyapron_1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 78.Cucumber-Salad-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 79.layered-crunchy-noodle-salad-131650-1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 80.Lebanese-salad-image-720x540.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 81.1200px-Salad_platter.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 82.winter-greens-and-citrus-salad-1811-p90.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 83.GRILLED-CHICKEN-AVOCADO-AND-MANGO-SALAD-3.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 84.nicoise-salad-I-howsweeteats.com-9.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 85.broccoli-salad-recipe-2.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 86.Green-Goddess-Salad-7-of-7-copy.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 87.chicken-caesar-pasta-salad-recipe.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 88.oil-and-vinegar-salad-dressing-recipe-995915-Final-5ba002b84cedfd00259287af.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 89.Vegan-Southwest-Pasta-Salad-Recipe-7.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 90.New-York-kale-and-chicken-Caesar-salad.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 91.broccoli-crunch-salad-I-howsweeteats.com-8.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 92.scottish-salmon-caesar-salad.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 93.IMG_4753.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 94.Caprese_Salad_Image_1_800_480_85_s_c1.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 95.Greek-Salad-with-Grilled-Chicken-foodiecrush.com-005.jpg',
   'C:\\Users\\Milosz\\data_food_classifier_MG\\slow_food 96.Grilled-Romaine-Salad-SQUARE.jpg'],
  ' diet food': []},
 57)

Image preparation

In [4]:
import numpy as np
from PIL import Image
from functions.food_classifier_plotting_functions import plot_accuracy_loss, plot_predictions_or_examples, plot_confusion_matrix
from functions.split_images import split_images
from functions.my_image_data_generator import my_image_data_generator
from functions.correct_predictions import correct_predictions
from functions.my_sequential import my_sequential
%matplotlib inline
Using TensorFlow backend.

Some images may have been saved with the wrong extension.

In [5]:
root_listdir = os.listdir(root_dir)
counter = 0

for index, element in enumerate(root_listdir):
    filename = root_dir + '/' + element
    try:
        im = Image.open(filename)
        im.verify() #I perform also verify, don't know if he sees other types o defects
        im.close() #reload is necessary in my case
    except (OSError, ValueError):
        os.remove(filename)
        root_listdir[index] = 0
        counter += 1

print('%d files deleted due to OSError and  ValueError.' %counter)
0 files deleted due to OSError and  ValueError.

Splitting images into train, test and valid sets.

In [6]:
root_listdir = [i for i in root_listdir if i != 0]
list_of_images_indices = np.random.choice(len(root_listdir), 60)
list_of_images_names = [root_listdir[i] for i in list_of_images_indices]

plot_predictions_or_examples(list_of_images_names, title="Random image from food class")
In [7]:
fast_food_list = [i for i in root_listdir if 'fast_food' in i]
slow_food_list = [i for i in root_listdir if 'slow_food' in i]

train_examples_fast, valid_examples_fast, test_examples_fast = split_images(fast_food_list, root_dir, '/fast_food/')
train_examples_slow, valid_examples_slow, test_examples_slow = split_images(slow_food_list, root_dir, '/slow_food/')

train_examples = train_examples_fast + train_examples_slow
valid_examples = valid_examples_fast + valid_examples_slow
test_examples = test_examples_fast + test_examples_slow

Classification models

In [8]:
%load_ext tensorboard

import tensorflow as tf
import keras
import random
import itertools
import datetime

from sklearn.metrics import confusion_matrix

from keras.preprocessing.image import ImageDataGenerator
from keras import applications, backend as K
from keras.models import  Model
from keras.layers import (
    Dense,
    Activation,
    Conv2D,
    MaxPool2D,
    Flatten,
    BatchNormalization,
    Dropout,
)
from keras.layers.core import Dense, Flatten
from keras.layers.normalization import BatchNormalization
from keras.optimizers import Adam, RMSprop
from keras.metrics import categorical_crossentropy
from keras_lr_finder import LRFinder
from keras.callbacks import TensorBoard
In [9]:
channels = 3
img_height = img_width = 224
batch_size = 32

train_folder = root_dir + "/train"
test_folder = root_dir + "/test"
valid_folder = root_dir + "/valid"

classes = ["slow_food", "fast_food"]

Logistic regression

First attempt should be simple so we will check Logistic Regression on our data.

In [10]:
train_generator = my_image_data_generator(
    train_folder, class_mode="binary", shuffle=True
)
test_generator = my_image_data_generator(test_folder, class_mode="binary", shuffle=True)

x_train, y_train = next(train_generator)
x_test, y_test = next(test_generator)
Found 1055 images belonging to 2 classes.
Found 226 images belonging to 2 classes.
In [11]:
from sklearn.linear_model import LogisticRegression

logistic = LogisticRegression(solver="liblinear")
logistic.fit(x_train.reshape(batch_size, -1), y_train)
Out[11]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
                   intercept_scaling=1, l1_ratio=None, max_iter=100,
                   multi_class='warn', n_jobs=None, penalty='l2',
                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,
                   warm_start=False)
In [12]:
y_pred = logistic.predict(x_test.reshape(len(x_test), -1))
np.count_nonzero(y_pred == y_test) / len(y_test)
Out[12]:
0.65625

Model accuracy is not satisfactory.

Build Fine-tuned VGG16 model

Now we will try to use pretrained model VGG16. The model achieves 92.7% top-5 test accuracy in ImageNet, which is a dataset of over 14 million images belonging to 1000 classes.

In [13]:
K.clear_session()
vgg16_model = applications.vgg16.VGG16()
vgg16_model.summary()
Model: "vgg16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 224, 224, 3)       0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 25088)             0         
_________________________________________________________________
fc1 (Dense)                  (None, 4096)              102764544 
_________________________________________________________________
fc2 (Dense)                  (None, 4096)              16781312  
_________________________________________________________________
predictions (Dense)          (None, 1000)              4097000   
=================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
_________________________________________________________________
In [14]:
model = my_sequential()
for layer in vgg16_model.layers[:-3]:
    model.add(layer)

We should pop dense layers.

In [15]:
for layer in model.layers:
    layer.trainable = False

model.add(Dense(2, activation="sigmoid"))
model.summary()
Model: "my_sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 25088)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 2)                 50178     
=================================================================
Total params: 14,764,866
Trainable params: 50,178
Non-trainable params: 14,714,688
_________________________________________________________________
In [16]:
train_batches = my_image_data_generator(train_folder, batch_size=10, classes=classes)
test_batches = my_image_data_generator(test_folder, batch_size=10, classes=classes)
valid_batches = my_image_data_generator(valid_folder, batch_size=4, classes=classes)
Found 1055 images belonging to 2 classes.
Found 226 images belonging to 2 classes.
Found 226 images belonging to 2 classes.

Train the fine-tuned VGG16 model

In [17]:
model.compile(Adam(lr=0.001), loss="categorical_crossentropy", metrics=["accuracy"])

log_dir = os.path.join(
    "logs",
    "fit",
    datetime.datetime.now().strftime("%Y%m%d-%H%M%S"),
)
tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)

history = model.fit_generator(
    train_batches,
    train_examples // 10,
    validation_data=valid_batches,
    validation_steps=valid_examples // 4 + 1,
    epochs=10,
    verbose=2,
    callbacks=[tensorboard_callback],
)

predictions = model.predict_generator(test_batches, test_examples // 10)
Epoch 1/10
 - 217s - loss: 0.7196 - accuracy: 0.4995 - val_loss: 0.6931 - val_accuracy: 0.5044
Epoch 2/10
 - 216s - loss: 0.6931 - accuracy: 0.5091 - val_loss: 0.6931 - val_accuracy: 0.5044
Epoch 3/10
 - 217s - loss: 0.6931 - accuracy: 0.4900 - val_loss: 0.6931 - val_accuracy: 0.5044
Epoch 4/10
 - 216s - loss: 0.6931 - accuracy: 0.5091 - val_loss: 0.6931 - val_accuracy: 0.5044
Epoch 5/10
 - 216s - loss: 0.6931 - accuracy: 0.4986 - val_loss: 0.6931 - val_accuracy: 0.5044
Epoch 6/10
 - 216s - loss: 0.6931 - accuracy: 0.5206 - val_loss: 0.6931 - val_accuracy: 0.5044
Epoch 7/10
 - 217s - loss: 0.6931 - accuracy: 0.4876 - val_loss: 0.6931 - val_accuracy: 0.5044
Epoch 8/10
 - 215s - loss: 0.6931 - accuracy: 0.5019 - val_loss: 0.6931 - val_accuracy: 0.5044
Epoch 9/10
 - 642s - loss: 0.6931 - accuracy: 0.4900 - val_loss: 0.6931 - val_accuracy: 0.5044
Epoch 10/10
 - 219s - loss: 0.6931 - accuracy: 0.5378 - val_loss: 0.6931 - val_accuracy: 0.5044
In [18]:
predictions = model.predict_generator(test_batches, test_examples // 10)
correct_predictions(test_batches, predictions)
Correct predictions: 0.4690265486725664
In [19]:
plot_accuracy_loss(history)
In [20]:
c = list(
    zip(
        [i.split("\\")[1] for i in test_batches.filenames],
        [1 if i[0] >= 0.5 else 0 for i in predictions],
    )
)
random.shuffle(c)

filenames, preds = zip(*c)
In [21]:
wrong_predictions = plot_predictions_or_examples(filenames, preds)
In [22]:
wrong_predictions
Out[22]:
['slow_food 61.fish-packet-asparagus-and-rice.jpg',
 'slow_food 28.light-dinners-coconut-shrimp-rice-1566498330.jpg',
 'slow_food 40.Al-Desko-Steak-Salad-Leftovers-Recipe.jpg',
 'slow_food 46.facebook-cover-art.jpg',
 'slow_food 10.rejsh0to_protein-rich-salads_625x300_27_September_19.jpg',
 'slow_food 83.insta-14-3.jpg',
 'slow_food 58.Stuffed-avocado.jpg',
 'slow_food 89.green-bean-bundles-6.jpg',
 'slow_food 61.20190719-140436-blackberry_79345.jpg',
 'slow_food 83.Stocksy_txpa269f3e29MP100_Medium_1257410-58c70b893df78c353cdeb0bd.jpg',
 'slow_food 77.a2679-1534700268-800.jpg',
 'slow_food 12.Healthy-Meal-Prep-Baked-Turkey-Meatballs--500x500.jpg',
 'slow_food 82.easy-kielbasa-sheet-pan-dinner-with-veggies-image.jpg',
 'slow_food 63.secret-to-a-healthy-heart.jpg',
 'slow_food 13.bacon-green-beans-side-dish-keto-gluten-free-whole30-paleo-4-of-4-1.jpg',
 'slow_food 3.1504633899842-recipeMainImagePath.jpg',
 'slow_food 31.salmon-518497_1280-1080x675.jpg',
 'slow_food 5.GM-Diet--Is-It-The-Best-Plan-For-Weight-Loss-In-7-Days.jpg',
 'slow_food 79.5321_4k.jpg',
 'slow_food 44.best-side-salad-3.jpg',
 'slow_food 54.wide_25590.jpg',
 'slow_food 58.032019__dirty_dozen_clean_15_pesti.2e16d0ba.fill-735x490.jpg',
 'slow_food 67.Plant-Based-Diets-blog-cover-300x300-2018-02.jpg',
 'slow_food 82.maxfitmeals_breakfastburrito2017_web.jpg',
 'slow_food 76.ThinkstockPhotos-179103931-Diet-2048x1024.jpg',
 'slow_food 45.Header_2_1600x.jpg']
In [23]:
test_labels = np.array([0 if "slow_food" in f else 1 for f in test_batches.filenames])[
    : len(predictions)
]
cm = confusion_matrix(test_labels, np.round(predictions[:, 0]))
In [24]:
plot_confusion_matrix(cm, classes, title="Confusion Matrix")
Confusion matrix, without normalization
[[  0 114]
 [  0 106]]

Model performed better than simple guessing, but it is still less accurate than Logistic Regression.

Sequential model

Pretrained model didn't pass the exam. Now we will try create our own model.

In [25]:
K.clear_session()
model = my_sequential()

model.add(
    Conv2D(
        8,
        kernel_size=(3, 3),
        padding="same",
        input_shape=(img_width, img_height, channels),
    )
)
model.add(Activation("relu"))
model.add(MaxPool2D(pool_size=(2, 2)))

model.add(Conv2D(16, kernel_size=(3, 3), padding="same"))
model.add(BatchNormalization())
model.add(Activation("relu"))
model.add(MaxPool2D(pool_size=(2, 2)))

model.add(Conv2D(32, kernel_size=(3, 3), padding="same"))
model.add(BatchNormalization())
model.add(Activation("relu"))
model.add(MaxPool2D(pool_size=(2, 2)))

model.add(Flatten())
model.add(Dense(2, activation="sigmoid"))
model.compile(optimizer="rmsprop", loss="binary_crossentropy", metrics=["accuracy"])
model.summary()
Model: "my_sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 224, 224, 8)       224       
_________________________________________________________________
activation_1 (Activation)    (None, 224, 224, 8)       0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 112, 112, 8)       0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 112, 112, 16)      1168      
_________________________________________________________________
batch_normalization_1 (Batch (None, 112, 112, 16)      64        
_________________________________________________________________
activation_2 (Activation)    (None, 112, 112, 16)      0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 56, 56, 16)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 56, 56, 32)        4640      
_________________________________________________________________
batch_normalization_2 (Batch (None, 56, 56, 32)        128       
_________________________________________________________________
activation_3 (Activation)    (None, 56, 56, 32)        0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 28, 28, 32)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 25088)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 2)                 50178     
=================================================================
Total params: 56,402
Trainable params: 56,306
Non-trainable params: 96
_________________________________________________________________
In [26]:
train_batches = my_image_data_generator(
    train_folder, classes=classes, shuffle=True
)
test_batches = my_image_data_generator(test_folder, classes=classes)
Found 1055 images belonging to 2 classes.
Found 226 images belonging to 2 classes.
In [27]:
model.fit_generator(train_batches, train_examples // batch_size, epochs=2)
Epoch 1/2
32/32 [==============================] - 36s 1s/step - loss: 3.1560 - accuracy: 0.6256
Epoch 2/2
32/32 [==============================] - 36s 1s/step - loss: 1.4072 - accuracy: 0.7395
Out[27]:
<keras.callbacks.callbacks.History at 0x1ec7207ba20>
In [28]:
y_pred = model.predict_generator(test_batches, test_examples // batch_size, workers=4)
correct_predictions(test_batches, y_pred)
Correct predictions: 0.504424778761062
In [29]:
c = list(
    zip(
        [i.split("\\")[1] for i in test_batches.filenames],
        [1 if i[0] >= 0.5 else 0 for i in y_pred],
    )
)
random.shuffle(c)

filenames_2, preds_2 = zip(*c)
In [30]:
wrong_predictions_2 = plot_predictions_or_examples(filenames_2, preds_2)
In [31]:
wrong_predictions_2
Out[31]:
['fast_food 76.1475-shutterstock_295119902.jpg',
 'fast_food 56.cold-fry-frites-patricia-wells-030617.jpg',
 'fast_food 47.P6101542.jpg',
 'fast_food 27.Big__fatty.jpg',
 'fast_food 32.FryCloseupOPT-480x270.jpg',
 'fast_food 28.IMG_5038.jpg',
 'fast_food 62.006.jpg',
 'fast_food 48.white-and-brown-bread-which-may-be-an-unhealthy-food.jpg',
 'fast_food 63.An-Unhealthy-Diet-Could-Hurt-Your-Brain-752x472.jpg',
 'fast_food 51.175841927-463310.jpg',
 'fast_food 70.french-fries-salted-egg-50461022.jpg',
 'fast_food 60.air-fries-recipe-for-the-best-healthy-fries_3729.jpg',
 'fast_food 81.Kebab-800x450.jpg',
 'fast_food 78.School+Junk+Food.jpg',
 'fast_food 64.Twister%2Bz%2BKFC%2B14.jpg',
 'fast_food 46.kfc.jpg',
 'fast_food 79.DvgP42uXgAETT7V.jpg',
 'fast_food 59.15339996.jpg',
 'fast_food 25.OCR-L-POPEYES-1022-1.jpg',
 'fast_food 51.BN-RN270_PORTIO_P_20170106154933.jpg',
 'fast_food 19.gf-1y8X-q6rp-7H9N_mcdonalds-przyjedzie-do-ciebie-664x442-nocrop.jpg',
 'fast_food 54.burito_s_kuricej_i_fasolyu_2.jpg',
 'fast_food 40.topimage-pizza-special17-800x500.jpg',
 'fast_food 13.pizza-930x530.jpg',
 'fast_food 28.10-Unhealthy-Foods-You-Need-To-Ditch-Right-Now.jpg',
 'fast_food 71.Difference-between-healthy-and-unhealthy-foods.jpg',
 'fast_food 80.Air-Fryer-Homemade-Fries-1.jpg',
 'fast_food 71.popeyes.jpg',
 'fast_food 74.Shoestring-French-Fries-4-500x500.jpg',
 'fast_food 73.berber-q-chicken-shish-kebab-1803100a-2ca2-4ae0-8082-9decec65d71c_s640x0_c4725x2760_l0x1787_q70_noupscale.jpg',
 'fast_food 47.Pizza-unhealthy-food.jpg']
In [32]:
test_labels = np.array([0 if "slow_food" in f else 1 for f in test_batches.filenames])[
    : len(y_pred)
]
cm = confusion_matrix(test_labels, np.round(y_pred[:, 0]))
In [33]:
plot_confusion_matrix(cm, classes, title="Confusion Matrix")
Confusion matrix, without normalization
[[114   0]
 [110   0]]

Model is much worse than previous ones. It means that simple model won't be enough, because we don't have big dataset.

Transfer learning

Last attempt is transfer learning. We will use VGG16 again but this time in other way.

In [34]:
K.clear_session()
train_batches_1 = my_image_data_generator(train_folder, class_mode=None)
train_batches_2 = my_image_data_generator(train_folder, class_mode=None)
valid_batches = my_image_data_generator(valid_folder, class_mode=None)
test_batches = my_image_data_generator(test_folder, class_mode="binary")
Found 1055 images belonging to 2 classes.
Found 1055 images belonging to 2 classes.
Found 226 images belonging to 2 classes.
Found 226 images belonging to 2 classes.
In [35]:
model = applications.VGG16(include_top=False, weights="imagenet")
model.summary()
Model: "vgg16"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, None, None, 3)     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, None, None, 64)    1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, None, None, 64)    36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, None, None, 64)    0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, None, None, 128)   73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, None, None, 128)   147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, None, None, 128)   0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, None, None, 256)   295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, None, None, 256)   590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, None, None, 256)   0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, None, None, 512)   1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, None, None, 512)   0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, None, None, 512)   2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, None, None, 512)   0         
=================================================================
Total params: 14,714,688
Trainable params: 14,714,688
Non-trainable params: 0
_________________________________________________________________

Training VGG16 on our data should give us the bottleneks of our images. In this case we will use VGG16 predictions to train our sequential model. It means that we changed sets of images into sets of bottlenecks which could be more helpful in detecting category.

In [36]:
bottleneck_features_train = model.predict_generator(
    train_batches_1, train_examples // batch_size, verbose=1, workers=4
)
bottleneck_features_valid = model.predict_generator(
    valid_batches, test_examples // batch_size, verbose=1, workers=4
)
bottleneck_features_test = model.predict_generator(
    test_batches, test_examples // batch_size, verbose=1, workers=4
)

bottleneck_features_train.shape
32/32 [==============================] - 177s 6s/step
7/7 [==============================] - 38s 5s/step
7/7 [==============================] - 40s 6s/step
Out[36]:
(1024, 7, 7, 512)
In [37]:
model = my_sequential()
model.add(Flatten(input_shape=bottleneck_features_train.shape[1:]))
model.add(Dense(256, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(1, activation="sigmoid"))

for layer in model.layers[:-1]:
    layer.trainable = False

model.compile(
    optimizer=RMSprop(learning_rate=0.01, rho=0.9),
    loss="binary_crossentropy",
    metrics=["accuracy"],
)

log_dir = os.path.join(
    "logs",
    "fit",
    datetime.datetime.now().strftime("%Y%m%d-%H%M%S"),
)
tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1)

model.summary()
Model: "my_sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_1 (Flatten)          (None, 25088)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 256)               6422784   
_________________________________________________________________
dropout_1 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 257       
=================================================================
Total params: 6,423,041
Trainable params: 257
Non-trainable params: 6,422,784
_________________________________________________________________
In [38]:
labels = np.array([0 if "slow_food" in f else 1 for f in train_batches_2.filenames])[
    : len(bottleneck_features_train)
]
val_labels = np.array([0 if "slow_food" in f else 1 for f in valid_batches.filenames])[
    : len(bottleneck_features_valid)
]
In [39]:
lr_finder = LRFinder(model)
lr_finder.find(bottleneck_features_train, labels, 0.0000001, 1, 200, 20)
lr_finder.plot_loss()
Epoch 1/20
1024/1024 [==============================] - 0s 255us/step - loss: 0.8079 - accuracy: 0.5078
Epoch 2/20
1024/1024 [==============================] - 0s 168us/step - loss: 0.8230 - accuracy: 0.4922
Epoch 3/20
1024/1024 [==============================] - 0s 191us/step - loss: 0.7936 - accuracy: 0.4893
Epoch 4/20
1024/1024 [==============================] - 0s 178us/step - loss: 0.8005 - accuracy: 0.5156
Epoch 5/20
1024/1024 [==============================] - 0s 175us/step - loss: 0.7965 - accuracy: 0.5205
Epoch 6/20
1024/1024 [==============================] - 0s 176us/step - loss: 0.8018 - accuracy: 0.4961
Epoch 7/20
1024/1024 [==============================] - 0s 174us/step - loss: 0.8204 - accuracy: 0.5049
Epoch 8/20
1024/1024 [==============================] - 0s 182us/step - loss: 0.8069 - accuracy: 0.4805
Epoch 9/20
1024/1024 [==============================] - 0s 168us/step - loss: 0.7981 - accuracy: 0.48340s - loss: 0.7901 - accuracy: 0.
Epoch 10/20
1024/1024 [==============================] - 0s 150us/step - loss: 0.8020 - accuracy: 0.4863
Epoch 11/20
1024/1024 [==============================] - 0s 149us/step - loss: 0.8139 - accuracy: 0.4775
Epoch 12/20
1024/1024 [==============================] - 0s 149us/step - loss: 0.7888 - accuracy: 0.4854
Epoch 13/20
1024/1024 [==============================] - 0s 142us/step - loss: 0.7959 - accuracy: 0.5176
Epoch 14/20
1024/1024 [==============================] - 0s 156us/step - loss: 1.2102 - accuracy: 0.4863
Epoch 15/20
 600/1024 [================>.............] - ETA: 0s - loss: 1.8664 - accuracy: 0.4867
In [40]:
lr_finder.plot_loss_change(
    sma=20, n_skip_beginning=15, n_skip_end=2, y_lim=(-0.05, 0.06)
)
In [41]:
model.fit(
    bottleneck_features_train,
    labels,
    validation_data=(bottleneck_features_valid[: len(val_labels)], val_labels),
    epochs=20,
    batch_size=batch_size,
)
for layer in model.layers[:-1]:
    layer.trainable = True

model.compile(
    optimizer=RMSprop(learning_rate=0.001, rho=0.9),
    loss="binary_crossentropy",
    metrics=["accuracy"],
)

history_2 = model.fit(
    bottleneck_features_train,
    labels,
    validation_data=(bottleneck_features_valid[: len(val_labels)], val_labels),
    epochs=20,
    batch_size=batch_size,
    callbacks=[tensorboard_callback],
)
Train on 1024 samples, validate on 224 samples
Epoch 1/20
1024/1024 [==============================] - 0s 341us/step - loss: 0.7660 - accuracy: 0.5107 - val_loss: 0.6624 - val_accuracy: 0.6161
Epoch 2/20
1024/1024 [==============================] - 0s 320us/step - loss: 0.7118 - accuracy: 0.5576 - val_loss: 0.6740 - val_accuracy: 0.5714
Epoch 3/20
1024/1024 [==============================] - 0s 318us/step - loss: 0.6760 - accuracy: 0.5967 - val_loss: 0.6752 - val_accuracy: 0.5491
Epoch 4/20
1024/1024 [==============================] - 0s 315us/step - loss: 0.6575 - accuracy: 0.6123 - val_loss: 0.7017 - val_accuracy: 0.5446
Epoch 5/20
1024/1024 [==============================] - 0s 320us/step - loss: 0.6534 - accuracy: 0.6357 - val_loss: 0.6257 - val_accuracy: 0.6562
Epoch 6/20
1024/1024 [==============================] - 0s 321us/step - loss: 0.6425 - accuracy: 0.6338 - val_loss: 0.6193 - val_accuracy: 0.6607
Epoch 7/20
1024/1024 [==============================] - 0s 326us/step - loss: 0.6545 - accuracy: 0.6270 - val_loss: 0.6803 - val_accuracy: 0.5670
Epoch 8/20
1024/1024 [==============================] - 0s 384us/step - loss: 0.6415 - accuracy: 0.6279 - val_loss: 0.6223 - val_accuracy: 0.6562
Epoch 9/20
1024/1024 [==============================] - 0s 328us/step - loss: 0.6300 - accuracy: 0.6621 - val_loss: 0.6519 - val_accuracy: 0.6250
Epoch 10/20
1024/1024 [==============================] - 0s 342us/step - loss: 0.6602 - accuracy: 0.6270 - val_loss: 0.6541 - val_accuracy: 0.5982
Epoch 11/20
1024/1024 [==============================] - 0s 319us/step - loss: 0.6431 - accuracy: 0.6357 - val_loss: 0.6762 - val_accuracy: 0.5804
Epoch 12/20
1024/1024 [==============================] - 0s 315us/step - loss: 0.6469 - accuracy: 0.6416 - val_loss: 0.6106 - val_accuracy: 0.6562
Epoch 13/20
1024/1024 [==============================] - 0s 317us/step - loss: 0.6266 - accuracy: 0.6504 - val_loss: 0.6183 - val_accuracy: 0.6473
Epoch 14/20
1024/1024 [==============================] - 0s 315us/step - loss: 0.6739 - accuracy: 0.6074 - val_loss: 0.6026 - val_accuracy: 0.6696
Epoch 15/20
1024/1024 [==============================] - 0s 340us/step - loss: 0.6656 - accuracy: 0.6230 - val_loss: 0.6041 - val_accuracy: 0.6786
Epoch 16/20
1024/1024 [==============================] - 0s 348us/step - loss: 0.6573 - accuracy: 0.6250 - val_loss: 0.6332 - val_accuracy: 0.6473
Epoch 17/20
1024/1024 [==============================] - 0s 319us/step - loss: 0.6282 - accuracy: 0.6367 - val_loss: 0.6004 - val_accuracy: 0.6875
Epoch 18/20
1024/1024 [==============================] - 0s 325us/step - loss: 0.6255 - accuracy: 0.6504 - val_loss: 0.6119 - val_accuracy: 0.6652
Epoch 19/20
1024/1024 [==============================] - 0s 324us/step - loss: 0.6500 - accuracy: 0.6387 - val_loss: 0.6122 - val_accuracy: 0.6741
Epoch 20/20
1024/1024 [==============================] - 0s 333us/step - loss: 0.6381 - accuracy: 0.6426 - val_loss: 0.6193 - val_accuracy: 0.6518
Train on 1024 samples, validate on 224 samples
Epoch 1/20
1024/1024 [==============================] - 2s 2ms/step - loss: 7.0027 - accuracy: 0.5645 - val_loss: 0.5039 - val_accuracy: 0.7946
Epoch 2/20
1024/1024 [==============================] - 2s 2ms/step - loss: 1.0776 - accuracy: 0.6787 - val_loss: 1.1147 - val_accuracy: 0.5580
Epoch 3/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.8585 - accuracy: 0.6904 - val_loss: 0.4512 - val_accuracy: 0.8036
Epoch 4/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.8361 - accuracy: 0.7334 - val_loss: 0.4642 - val_accuracy: 0.7500
Epoch 5/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.6008 - accuracy: 0.7627 - val_loss: 0.5468 - val_accuracy: 0.7589
Epoch 6/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.5693 - accuracy: 0.7930 - val_loss: 0.3864 - val_accuracy: 0.8259
Epoch 7/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.3972 - accuracy: 0.8408 - val_loss: 0.4960 - val_accuracy: 0.7812
Epoch 8/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.3658 - accuracy: 0.8564 - val_loss: 0.4218 - val_accuracy: 0.8036
Epoch 9/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.2635 - accuracy: 0.8936 - val_loss: 1.2127 - val_accuracy: 0.6205
Epoch 10/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.2591 - accuracy: 0.8896 - val_loss: 0.5767 - val_accuracy: 0.7679
Epoch 11/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.2713 - accuracy: 0.8965 - val_loss: 0.4964 - val_accuracy: 0.8036
Epoch 12/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.2399 - accuracy: 0.9180 - val_loss: 0.5987 - val_accuracy: 0.7679
Epoch 13/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.1860 - accuracy: 0.9336 - val_loss: 0.4666 - val_accuracy: 0.8259
Epoch 14/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.1838 - accuracy: 0.9355 - val_loss: 0.6134 - val_accuracy: 0.7857
Epoch 15/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.1536 - accuracy: 0.9502 - val_loss: 0.6403 - val_accuracy: 0.7857
Epoch 16/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.1643 - accuracy: 0.9463 - val_loss: 0.5881 - val_accuracy: 0.7902
Epoch 17/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.1121 - accuracy: 0.9697 - val_loss: 0.6115 - val_accuracy: 0.8259
Epoch 18/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.2150 - accuracy: 0.9492 - val_loss: 0.7794 - val_accuracy: 0.7768
Epoch 19/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.0903 - accuracy: 0.9668 - val_loss: 0.8682 - val_accuracy: 0.7723
Epoch 20/20
1024/1024 [==============================] - 2s 2ms/step - loss: 0.1591 - accuracy: 0.9668 - val_loss: 0.7164 - val_accuracy: 0.8125
In [42]:
test_labels = np.array([0 if "slow_food" in f else 1 for f in test_batches.filenames])[
    : len(bottleneck_features_test)
]
y_test_pred = model.predict_classes(bottleneck_features_test)
accuracy = np.count_nonzero(
    y_test_pred[: len(test_labels)].ravel() == test_labels
) / len(test_labels)

print("\nThe accuracy is: " + str(accuracy))
The accuracy is: 0.8035714285714286
In [43]:
plot_accuracy_loss(history_2)

This model is the most promising one.

In [44]:
cm = confusion_matrix(test_labels, np.round(y_test_pred[: len(test_labels)]))
In [45]:
plot_confusion_matrix(cm, classes, title="Confusion Matrix")
Confusion matrix, without normalization
[[82 30]
 [14 98]]
In [46]:
plot_confusion_matrix(cm, classes, title="Confusion Matrix", normalize=True)
Normalized confusion matrix
[[0.73214286 0.26785714]
 [0.125      0.875     ]]

Prediction on the test set are also much more accurate thane previous ones.

In [47]:
c = list(zip([i.split("\\")[1] for i in test_batches.filenames], y_test_pred))
random.shuffle(c)

filenames_3, preds_3 = zip(*c)
In [48]:
wrong_predictions_3 = plot_predictions_or_examples(filenames_3, preds_3)
In [49]:
wrong_predictions_3
Out[49]:
['slow_food 9.salmon.jpg',
 'slow_food 42.47425871_401.jpg',
 'fast_food 66.n_9726-1.jpg',
 'slow_food 34.healthy-fruits-1296x728.jpg',
 'slow_food 72.Classic-Chicken-Salad-2.jpg',
 'slow_food 76.Moroccan-Chicken-with-Green-Beans.jpg',
 'slow_food 71.74b153_fit-meal-catering-dietetyczny-lodz-lodzkie-zdjecia.jpg',
 'slow_food 79.fresh_fruit_salad_61942_16x9.jpg',
 'fast_food 13.pizza-930x530.jpg',
 'fast_food 54.f42dbda63b75be1fc3251648954e1426.jpg',
 'slow_food 63.secret-to-a-healthy-heart.jpg',
 'slow_food 38.iStock-588354332-5a4baf7c7bb28300379b7522.jpg',
 'slow_food 82.easy-kielbasa-sheet-pan-dinner-with-veggies-image.jpg']

Tensorboard

In [53]:
from tensorboard import notebook
notebook.list() # View open TensorBoard instances
Known TensorBoard instances:
  - port 6006: logdir logs/fit (started 10:40:12 ago; pid 21392)
In [54]:
notebook.display(port=6006, height=1000) 
Selecting TensorBoard with logdir logs/fit (started 10:40:23 ago; port 6006, pid 21392).